The amount of data residing in social media currently untapped is certainly limitless as millions of people are constantly posting a message or the other to public forums on the internet. Twitter being one of the largest social media networks with over 336 million monthly active users has proven to be a fertile ground for harvesting opinion from multiple people. This work explores how opinion can be extracted from tweets to discover people’s view concerning a certain subject matter. It focuses mainly on overcoming the limitation of the current approach to social media sentiment based mining for decision making which is that opinions derived from multiple sources are limited to available connections on the social media platforms and lack of improved accuracy of mined opinions. In order to achieve this, the proposed framework provides a platform to mine opinions from more than the available friends and connections on the social media platform and in addition, improve the quality of the opinion mined by implementing supervised learning algorithms with learning by induction in Twitter data analysis. In this research, three different supervised machine learning algorithms were applied to a dataset curated by graduate students at Stanford in order to accurately classify tweets into either positive or negative sentiment based on its content. It was discovered that Maximum Entropy had the highest accuracy of 83.5% among the three algorithms. The research has provided a web application which would enable users such as CEOs, Market Analysts, and random users make quality decision based on others’ opinions.
Abstract-Multiple Choice Item Generator is a research focused on generating grammatically and semantically correct questions and generating plausible distractors. The system's framework has three main modules: pre-processing, question generation and distractor selection. Pre-processing module implements Hobbs Algorithm for anaphora resolution and direct to indirect speech conversion for handling quoted statements. In generating questions, question overgeneration and ranking framework of Heilman and Smith is used. The distractor selection module uses collocation extraction and Wordnet-based methods such as Lin's semantic similarity measure, hypernyms, hyponyms and maximal bipartite graph matching .Index Terms-Hobbs algorithm, question overgeneration, multiple-choice item generator.
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